We have the pizza shop application working on our machine, but it’s no good running there because only we can use it; now we need to get it into production. In this chapter, we’ll be covering some of the things to keep in mind when moving a real-time analytics application from your local machine into production.
When moving into production, we’re optimizing for performance, reliability, and cost.
This isn’t the most exciting chapter, but the information included here will hopefully be useful for helping you run your applications in a live environment.
Before we get into the nitty-gritty, let’s remind ourselves about the high-level design of a real-time analytics system, as shown in Figure 11-1.
Figure 11-1. Real-time analytics stack
Preproduction
Before going into production, we need to design our production architecture, which includes capacity planning and choosing our deployment platform.
Capacity Planning
Capacity planning is the process of determining the amount of required resources, mostly in terms of computing power and storage. This isn’t a one-time task, but rather a continuous process that requires regular monitoring and adjustment to adapt to changing demands and requirements.
We’ll have to collaborate with a variety of stakeholders, including operations and business users, to understand their needs and goals.
Figure 11-2 shows ...
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